A device for detecting a driving event of a vehicle, having a triaxial acceleration sensor and a processing unit. The device may be fixedly installed on the vehicle. The processing unit is configured to detect a plurality of acceleration values within a specific time span using the acceleration sensor, carry out a wavelet transform of the acceleration values to determine first coefficients, and compare the first coefficients at least to stored second coefficients. The second coefficients represent a predefined driving event. The processing unit is configured to detect the driving event represented by the second coefficients as an occurred driving event when the first coefficients are in agreement with the second coefficients, and is configured to determine characteristic parameters of the detected acceleration values, to ascertain a specific mother wavelet as a function of the characteristic parameters, and to carry out the wavelet transform based on the ascertained mother wavelet.

A system and method to perform lane position sensing and tracking in a vehicle include using a first camera with a first field of view of a first side of the vehicle and a second camera with a second field of view on a second side of the vehicle to capture images. The method includes determining if a first lane line on a first side of the vehicle and a second lane line on the second side of the vehicle are both visible, and based on the first lane line and the second lane line being visible, determining, a position of the vehicle in a lane between the first lane line and the second lane line and determining a trajectory of the first lane line and the second lane line and of the vehicle.

Kalman filter based road grade estimation techniques use models of a longitudinal accelerometer, an angular pitch rate gyroscope, and a velocity sensor and their outputs, and fuses the sensor measurements to optimally estimate the road grade. The proposed Kalman filter formulation is unique in that it uses a mathematical model of the sensors where the gyroscope output is considered as the input and the combined accelerometer and velocity sensor output is considered as the output of the model whose states are to be estimated. By using this unique second-order state space model, a Kalman filter based estimation algorithm is developed to estimate road grade accurately in real-time. This estimated road grade is then being utilized by various vehicle efficiency and/or safety systems to improve vehicle efficiency and/or vehicle safety.

There is provided a measurement device including a data acquisition unit that acquires pieces of first to third data output from first to third sensors provided on a structure, an abnormality determination unit that determines whether or not each of the sensors is abnormal, a moving object detection unit that detects a moving object based on at least one of the first data and the second data, and a displacement amount calculation unit that calculates a displacement amount of the structure based on the third data, in which, when the first sensor provided on a main girder closest to an i-th lane of the structure or a main girder second closest to the i-th lane is not abnormal, the moving object detection unit detects the moving object moving on the i-th lane based on the first data output from the first sensor.

A slope detection system (10, 19, 185C) for a vehicle (100) is provided. The system has a processor (10, 19) that receives, from one or more sensors (185C) arranged to capture data in respect of terrain ahead of the vehicle, terrain information indicative of the topography of an area extending ahead of the vehicle (100). The processor (10, 19), in dependence upon a predicted path of the vehicle (100) over the terrain extending ahead of the vehicle (100), generates, for the predicted path of the vehicle (100), information indicative of an angle of slope of the predicted path, being the slope of the predicted path along a direction of travel of the vehicle (100).

A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet also includes an inertial movement unit (IMU) configured to collect helmet motion data of a rider of the vehicle and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle and determine a rider attention state utilizing the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU.

A posture estimation device estimates a posture of a movable body based on acceleration information based on a posture change of the movable body and angular velocity information based on the posture change of the movable body. The posture estimation device includes a storage unit that stores the acceleration information, the angular velocity information, and a plurality of posture parameters related to a movement of the movable body, a parameter control unit that selects a selection posture parameter from the plurality of posture parameters, and a posture calculation unit that estimates the posture of the movable body by using the acceleration information, the angular velocity information, and the selection posture parameter.

A method of determining a position of a mobile device in a vehicle during a drive includes measuring at least one first acceleration magnitude of the mobile device in a gravity direction with at least one sensor of the mobile device, measuring at least one second acceleration magnitude of the mobile device in the gravity direction with the at least one sensor of the mobile device, the at least one second acceleration magnitude separated in time from the at least one first acceleration magnitude, comparing the at least one first acceleration magnitude with the at least one second acceleration magnitude, and based on a result of the comparing, predicting the position of the mobile device in the vehicle.

A drive and control system for a lawn tractor includes a CAN-Bus network, a vehicle controller, a pair of hydrostatic or electric transaxles controlled by respective electronic drive controllers, and one or more steering and drive input devices coupled to respective sensor(s) for sensing user steering and drive inputs. The vehicle controller communicates with one or more vehicle sensors and one or more vehicle controllers that control one or more vehicle components via the CAN-Bus network. The vehicle controller processes the user's steering and drive inputs and posts on the CAN-Bus network digital drive signals configured to obtain the desired speed and direction of motion of the lawn tractor. The electronic drive controllers convert the digital drive signals to appropriate signals for driving the hydrostatic transaxles or the electric transaxles, as equipped, based on tunable motion parameters to obtain the desired speed and direction of motion of the lawn tractor.

A drive and control system for a lawn tractor includes a CAN-Bus network, a vehicle controller, a pair of hydrostatic or electric transaxles controlled by respective electronic drive controllers, and one or more steering and drive input devices coupled to respective sensor(s) for sensing user steering and drive inputs. The vehicle controller communicates with one or more vehicle sensors and one or more vehicle controllers that control one or more vehicle components via the CAN-Bus network. The vehicle controller processes the user's steering and drive inputs and posts on the CAN-Bus network digital drive signals configured to obtain the desired speed and direction of motion of the lawn tractor. The electronic drive controllers convert the digital drive signals to appropriate signals for driving the hydrostatic transaxles or the electric transaxles, as equipped, based on tunable motion parameters to obtain the desired speed and direction of motion of the lawn tractor.